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Comment by drewbug01 | Hacker News Reader

I really appreciate comparisons like this, although I find myself wanting to know more about why certain things are listed the way they are.

For example, pgvector is listed as not having role-based access control, but the Postgres manual dedicates an entire chapter to it: https://www.postgresql.org/docs/current/user-manag.html

Hence why I’d be interested to know more about the supporting details for the different categories. It may help uncover some inadvertent errors in the analysis, but also would just serve as a useful jumping-off point for people doing their own research as well.

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Totally agree with the puzzling assortment of a rubric. PostgreSQL supports role based-access control, RBAC. Not to mention, with PostgreSQL and the pgvector extension, you have a whole list of languages ready to use it:

C++ pgvector-cpp C# pgvector-dotnet Crystal pgvector-crystal Dart pgvector-dart Elixir pgvector-elixir Go pgvector-go Haskell pgvector-haskell Java, Scala pgvector-java Julia pgvector-julia Lua pgvector-lua Node.js pgvector-node Perl pgvector-perl PHP pgvector-php Python pgvector-python R pgvector-r Ruby pgvector-ruby, Neighbor Rust pgvector-rust Swift pgvector-swift

Wonder how many of those other Vector databases play nice.

That stood out to me as well. I've been playing with pgvector, and there's no reason you can't use row/table role-based security.

I think there's an unmentioned benefit to using something like pgvector also. You don't need a separate relational database! In fact you can have foreign keys to your vectors/embeddings which is super powerful to me.

Same for Developer experience. If you used Postgres or any other relational db (which I think covers a large % of devs), you could easily argue the dev experience is 3/3 for pgvector.

Not only 3/3 but also includes full text search built in. Tables look like:

    MyThingEmbedding
    ______
    id primary key
    mything_id integer -- fkey to mything table
    embedding vector(1536)
    fulltext tsvector

    GIN index on tsvector
    HSNW index on embedding

Then you can pull results that match either the tsvector AND/OR the similarity with a single query, and it's pretty performant. You can also choose at the query level whether you want exact matching or fuzzy.

Possibly / quite probably whoever wrote this knows very little about postgres.

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